Najah Al-shanableh
@aabu.edu.jo
Computer Science
Al al-Bayt University
Experienced in teaching with a demonstrated history of working in the higher education industry. Skilled in ERP Implementations, Coaching, Software Engineering, Data Science, and Website Design. Strong education professional with an Interdisciplinary Doctorate focused in Healthcare Data Mining from New Mexico State University.
EDUCATION
Interdisciplinary Doctorate, Computer Science , NursingInterdisciplinary Doctorate, Computer Science , Nursing
New Mexico State University, 2011 - 2018
Master's degree, Computer ScienceMaster's degree, Computer Science
Al al-Bayt University, 2007 - 2010
Bachelor's degree, Software EngineeringBachelor's degree, Software Engineering
The Hashemite University, 2001 - 2005
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Science Applications, Artificial Intelligence, Multidisciplinary
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Emad Tariq, Iman Akour, Najah Al-Shanableh, Enass Khalil Alquqa, Nidal Alzboun, Sulieman Ibraheem Shelash Al-Hawary, and Muhammad Turki Alshurideh
Growing Science
In this digital age, fraudulent practices are among the most challenging that organizations must be aware of due to the increasing use of online transactions. This also applies to the banking sector whose business has become more complex with the recent developments in information and communication technology, which has changed the nature of bank fraud requiring advanced prevention measures. From this perspective, this paper aims to determine how cybersecurity affects fraud prevention for Jordanian commercial banks. A five-dimensional NIST cybersecurity framework was used. The research data was collected from 173 information technology managers in commercial banks listed on the Amman Stock Exchange. Structural equation modeling (SEM) was applied to investigate research hypotheses. The results of the research demonstrated the significant impact of cybersecurity in fraud prevention, especially detect function which had the largest impact among the dimensions of cybersecurity. Therefore, a set of recommendations were formulated for policymakers in Jordanian commercial banks, the most important of which is the adoption of multi-factor authentication (MFA) approaches for customer accounts, employee access, and biometric systems that add an additional layer of protection and make access to sensitive information to unauthorized individuals more difficult.
Mazen Alzyoud, Raed Alazaidah, Mohammad Aljaidi, Ghassan Samara, Mais Haj Qasem, Muhammad Khalid, and Najah Al-Shanableh
Growing Science
Diabetes Mellitus (DM) is a frequent condition in which the body's sugar levels are abnormally high for an extended length of time. It is a major cause of death with high mortality rates and the second leading cause of total years lived with disability worldwide. Its seriousness comes from its long-term complications, including nephropathy, retinopathy, and neuropathy leading to kidney failure, poor vision and blindness, and peripheral sensory loss, respectively. Such conditions are life-threatening and affect patients’ quality of life. Therefore, this paper aims to identify the most relevant features in the diagnosis of DM and identify the best classifier that can efficiently diagnose DM based on a set of relevant features. To achieve this, four different feature selection methods have been utilized. Moreover, twelve different classifiers that belong to six learning strategies have been evaluated using two datasets and several evaluation metrics such as Accuracy, Precision, Recall, F1-measure, and ROC area. The obtained results revealed that the correlation attribute evaluation method would be the best choice to handle the task of feature selection and ranking for the considered datasets, especially when considering the Accuracy metric. Furthermore, MultiClassClassifier would be the best classifier to handle Diabetes datasets, especially when considering True Positive, precision, and Recall metrics.
Mazen Alzyoud, Najah Al-Shanableh, Saleh Alomar, As’ad Mahmoud As’adAlnaser, Akram Mustafad, Ala’a Al-Momani, and Sulieman Ibraheem Shelash Al-Hawary
Growing Science
The growing significance of Artificial Intelligence (AI) across different fields highlights the essential role of user acceptance, as the success of this technology largely depends on its adoption and practical use by individuals. This research aims to examine how perceived cybersecurity, novelty value, and perceived trust affect students' willingness to accept AI in educational settings. The study's theoretical basis is the AI Device Use Acceptance (AIDUA) model. Using structural equation modeling, the study tested hypothesized relationships using data from 526 students at Jordanian universities. The results showed that social influence is positively associated with performance expectancy, while perceived cybersecurity is positively related to both performance and effort expectancy. Novelty value is positively associated with performance expectancy but a negative one with effort expectancy. Additionally, effort and performance expectancy significantly influence perceived trust and the willingness to accept AI. Moreover, perceived trust has a notable positive effect on the willingness to accept AI in education. These findings provide valuable guidance for the creation and improvement of AI-driven educational systems in universities, contributing to the broader understanding of AI technology acceptance in the educational field.
Najah Al-shanableh, Mazen Alzyoud, Saleh Alomar, Yousef Kilani, Eman Nashnush, Sulieman Al-Hawary, and Ala’a Al-Momani
Growing Science
While many small and medium enterprises (SMEs)recognize the benefits of Big Data Analytics (BDA) for digital transformation, they face challenges in implementing this technology, highlighting the need for more research on its adoption by SMEs. The objective of this study is to amalgamate the Technology Organization Environment (TOE) framework with the Diffusion of Innovation (DOI) theory, aiming to dissect the factors that sway BDA adoption in Jordanian SMEs. Additionally, the study delves into how perceived usefulness impacts this adoption process. Utilizing structural equation modeling, the study examined data from 388 managers in Jordan. The study validates all its hypotheses, revealing that variables like relative advantage, compatibility, complexity, top management support, competitive pressure, and security influence perceived usefulness, which subsequently has a positive impact on BDA adoption. This research presents a range of theoretical and practical insights.
R. Alazaidah, A. Al-Shaikh, M. Al-Mousa, H. Khafajah, G. Samara, M. Alzyoud, N. Al-Shanableh and S. Almatarneh
Natural Sciences Publishing
: Phishing is a cybercrime that is constantly increasing in the recent years due to the increased use of the Internet and its applications. It is one of the most common types of social engineering that aims to disclose or steel users sensitive or personal information. In this paper, two main objectives are considered. The first is to identify the best classifier that can detect phishing among twenty-four different classifiers that represent six learning strategies. The second objective aims to identify the best feature selection method for websites phishing datasets. Using two datasets that are related to Phishing with different characteristics and considering eight evaluation metrics, the results revealed the superiority of RandomForest, FilteredClassifier, and J-48 classifiers in detecting phishing websites. Also, InfoGainAttributeEval method showed the best performance among the four considered feature selection methods.
Raed Alazaidah, Mazen Alzyoud, Najah Al-Shanableh, and Haneen Alzoubi
AIP Publishing
Noha Al-Shdayfat, Ahlam Alnatour, Raya Alhusban, Dalal Yehia, Najah M Al-shanableh, Arwa Alsaraireh, and Ja’far M. Alkhawaldeh
Bentham Science Publishers Ltd.
Aims: The current study investigates the student nurses' attitudes and opinions towards their health promotion role during the COVID-19 pandemic using social media. Background: Social media and networking have become the most secure modes of communication among health care providers and their clients during the COVID-19 pandemic all over the world. However, it is the primary means of disseminating health information about disease prevention and control. Methods: A cross-sectional study was conducted on 296 student nurses aged 19-49 enrolled at twelve Jordanian universities (6 public and six private) in Jordan. The research team developed the self-administered questionnaire to explore the student nurses' attitudes towards their health promotion role during the COVID-19 pandemic using social media and the Internet. Results: Findings revealed that the student nurses had positive attitudes towards their health promotion role during the COVID-19 pandemic. The majority of student nurses are using social media to raise the awareness of their community about COVID-19 prevention. Conclusion: The current research findings provide baseline data on the student nurses' attitudes about the proper utilization of social media to enhance their community health about COVID-19. Given the student nurses' positive attitudes about their role in COVID-19 health promotion, we strongly recommend that they be provided with the necessary knowledge and skills to demonstrate effective health education.
N. Al-Shanableh, H. Al-Zoubi, and M. Al Rababaa
Praise Worthy Prize
Sequence alignment is a way of arranging primary sequences of DNA, RNA, or protein to identify regions of similarity. This region may be a consequence of functional, structural, or evolutionary relationships between the sequences. An algorithm is proposed for finding approximate local similarities in DNA sequences (AFALS-N). This algorithm is capable of finding the similarity between two sequences by generating all the possible words in the first sequence, then finding the exact matches in the second sequence. The selection of the obtained results is essential to reduce the number of possible results that in turn reduces the searching time. Results show that the proposed algorithm has reduced the searching time to an average of 20% in regard to PatternHunter algorithm. The objective of this work was evident by maintaining balance between the execution time and the size of seeds and the sensitivity. Improved execution time with 66% of sensitivity are obtained with the same word size as those used in other algorithms